{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:34:09Z","timestamp":1726043649893},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304836"},{"type":"electronic","value":"9783030304843"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30484-3_53","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T23:02:47Z","timestamp":1567983767000},"page":"673-686","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evaluation of Domain Adaptation Approaches for Robust Classification of Heterogeneous Biological Data Sets"],"prefix":"10.1007","author":[{"given":"Michael","family":"Schneider","sequence":"first","affiliation":[]},{"given":"Lichao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Carsten","family":"Marr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"53_CR1","unstructured":"Golkov, V., et al.: Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images. In: Advances in Neural Information Processing Systems, pp. 4222\u20134230 (2016)"},{"issue":"1","key":"53_CR2","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1002\/prot.340190108","volume":"19","author":"Burkhard Rost","year":"1994","unstructured":"Rost, B., Sander, C.: Combining evolutionary information and neural networks to predict protein secondary structure. Proteins Struct. Funct. Bioinform. 19(1), 55\u201372 (1994). \n                      https:\/\/doi.org\/10.1002\/prot.340190108","journal-title":"Proteins: Structure, Function, and Genetics"},{"issue":"6218","key":"53_CR3","doi-asserted-by":"publisher","first-page":"1254806","DOI":"10.1126\/science.1254806","volume":"347","author":"HY Xiong","year":"2015","unstructured":"Xiong, H.Y., et al.: The human splicing code reveals new insights into the genetic determinants of disease. Science 347(6218), 1254806 (2015). \n                      https:\/\/doi.org\/10.1126\/science.1254806","journal-title":"Science"},{"issue":"6769","key":"53_CR4","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/35000501","volume":"403","author":"AA Alizadeh","year":"2000","unstructured":"Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769), 503\u2013511 (2000). \n                      https:\/\/doi.org\/10.1038\/35000501","journal-title":"Nature"},{"issue":"7461","key":"53_CR5","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/nature12346","volume":"500","author":"M Helmstaedter","year":"2013","unstructured":"Helmstaedter, M., Briggman, K.L., Turaga, S.C., Jain, V., Seung, H.S., Denk, W.: Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500(7461), 168\u2013174 (2013). \n                      https:\/\/doi.org\/10.1038\/nature12346","journal-title":"Nature"},{"issue":"4","key":"53_CR6","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1038\/nmeth.4182","volume":"14","author":"F Buggenthin","year":"2017","unstructured":"Buggenthin, F., et al.: Prospective identification of hematopoietic lineage choice by deep learning. Nat. Methods 14(4), 403 (2017). \n                      https:\/\/doi.org\/10.1038\/nmeth.4182","journal-title":"Nat. Methods"},{"key":"53_CR7","doi-asserted-by":"publisher","first-page":"10256","DOI":"10.1038\/ncomms10256","volume":"7","author":"T Blasi","year":"2016","unstructured":"Blasi, T., et al.: Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7, 10256 (2016). \n                      https:\/\/doi.org\/10.1038\/ncomms10256","journal-title":"Nat. Commun."},{"issue":"10","key":"53_CR8","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1038\/nrg2825","volume":"11","author":"JT Leek","year":"2010","unstructured":"Leek, J.T., et al.: Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11(10), 733 (2010). \n                      https:\/\/doi.org\/10.1038\/nrg2825","journal-title":"Nat. Rev. Genet."},{"issue":"12","key":"53_CR9","doi-asserted-by":"publisher","first-page":"i105","DOI":"10.1093\/bioinformatics\/btu279","volume":"30","author":"C Bernau","year":"2014","unstructured":"Bernau, C., et al.: Cross-study validation for the assessment of prediction algorithms. Bioinformatics 30(12), i105\u2013i112 (2014). \n                      https:\/\/doi.org\/10.1093\/bioinformatics\/btu279","journal-title":"Bioinformatics"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Patricia, N., Caputo, B.: Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1442\u20131449 (2014)","DOI":"10.1109\/CVPR.2014.187"},{"issue":"10","key":"53_CR11","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"53_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","volume":"32","author":"VM Patel","year":"2015","unstructured":"Patel, V.M., Gopalan, R., Li, R., Chellappa, R.: Visual domain adaptation: a survey of recent advances. IEEE Signal Process. Magaz. 32(3), 53\u201369 (2015). \n                      https:\/\/doi.org\/10.1109\/MSP.2014.2347059","journal-title":"IEEE Signal Process. Magaz."},{"key":"53_CR13","doi-asserted-by":"publisher","unstructured":"Hwa, R.: Supervised grammar induction using training data with limited constituent information. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 73\u201379. Association for Computational Linguistics, Stroudsburg (1999). \n                      https:\/\/doi.org\/10.3115\/1034678.1034699","DOI":"10.3115\/1034678.1034699"},{"key":"53_CR14","unstructured":"Gildea, D.: Corpus variation and parser performance. In: Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, pp. 167\u2013202 (2001)"},{"key":"53_CR15","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1613\/jair.1872","volume":"26","author":"H Daume III","year":"2006","unstructured":"Daume III, H., Marcu, D.: Domain adaptation for statistical classifiers. J. Artif. Intell. Res. 26, 101\u2013126 (2006)","journal-title":"J. Artif. Intell. Res."},{"key":"53_CR16","unstructured":"Daum\u00e9 III, H.: Frustratingly easy domain adaptation. In: ACL, p. 256 (2007)"},{"key":"53_CR17","doi-asserted-by":"publisher","unstructured":"Laing, E.E., M\u00f6ller-Levet, C.S., Poh, N., Santhi, N., Archer, S.N., Dijk, D.J.: Blood transcriptome based biomarkers for human circadian phase. eLife 6, e20214 (2017). \n                      https:\/\/doi.org\/10.7554\/eLife.20214","DOI":"10.7554\/eLife.20214"},{"key":"53_CR18","doi-asserted-by":"publisher","unstructured":"Chen, L., et al.: Identification of breast cancer patients based on human signaling network motifs. Sci. Rep. 3 (2013). \n                      https:\/\/doi.org\/10.1038\/srep03368","DOI":"10.1038\/srep03368"},{"issue":"3","key":"53_CR19","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/bioinformatics\/btm595","volume":"24","author":"X Wang","year":"2008","unstructured":"Wang, X., El Naqa, I.M.: Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24(3), 325\u2013332 (2008). \n                      https:\/\/doi.org\/10.1093\/bioinformatics\/btm595","journal-title":"Bioinformatics"},{"key":"53_CR20","unstructured":"Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)"},{"key":"53_CR21","unstructured":"Daum\u00e9, III, H., Kumar, A., Saha, A.: Frustratingly easy semi-supervised domain adaptation. In: Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing, pp. 53\u201359. Association for Computational Linguistics (2010)"},{"issue":"3","key":"53_CR22","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). \n                      https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach. Learn."},{"key":"53_CR23","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144\u2013152. ACM (1992)","DOI":"10.1145\/130385.130401"},{"issue":"4","key":"53_CR24","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427\u2013437 (2009). \n                      https:\/\/doi.org\/10.1016\/j.ipm.2009.03.002","journal-title":"Inf. Process. Manage."},{"issue":"9","key":"53_CR25","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/nmeth.1486","volume":"7","author":"M Held","year":"2010","unstructured":"Held, M., et al.: Cell cognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat. Methods 7(9), 747\u2013754 (2010). \n                      https:\/\/doi.org\/10.1038\/nmeth.1486","journal-title":"Nat. Methods"},{"issue":"1","key":"53_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). \n                      https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"53_CR27","unstructured":"Rosenstein, M.T., Marx, Z., Kaelbling, L.P., Dietterich, T.G.: To transfer or not to transfer. In: NIPS 2005 Workshop on Transfer Learning, vol. 898, pp. 1\u20134 (2005)"},{"key":"53_CR28","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328 (2014)"},{"key":"53_CR29","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-58347-1_10","volume-title":"Domain Adaptation in Computer Vision Applications","author":"Y Ganin","year":"2017","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 189\u2013209. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-58347-1_10"},{"key":"53_CR30","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2208\u20132217 (2017)"},{"key":"53_CR31","unstructured":"Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: Advances in Neural Information Processing Systems, vol. 30, pp. 506\u2013516 (2017)"},{"key":"53_CR32","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Bilen, H., Vedaldi, A.: Efficient parametrization of multi-domain deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8119\u20138127 (2018)","DOI":"10.1109\/CVPR.2018.00847"},{"key":"53_CR33","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4068\u20134076 (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"53_CR34","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"53_CR35","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems, vol. 31, pp. 1640\u20131650 (2018)"},{"issue":"3","key":"53_CR36","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1093\/bioinformatics\/btm611","volume":"24","author":"L Jacob","year":"2007","unstructured":"Jacob, L., Vert, J.P.: Efficient peptide-MHC-I binding prediction for alleles with few known binders. Bioinformatics 24(3), 358\u2013366 (2007). \n                      https:\/\/doi.org\/10.1093\/bioinformatics\/btm611","journal-title":"Bioinformatics"},{"key":"53_CR37","unstructured":"Schweikert, G., R\u00e4tsch, G., Widmer, C., Sch\u00f6lkopf, B.: An empirical analysis of domain adaptation algorithms for genomic sequence analysis. In: Advances in Neural Information Processing Systems, pp. 1433\u20131440 (2009)"},{"key":"53_CR38","unstructured":"Widmer, C., R\u00e4tsch, G.: Multitask learning in computational biology. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 207\u2013216 (2012)"},{"issue":"1","key":"53_CR39","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s13218-013-0283-y","volume":"28","author":"C Widmer","year":"2014","unstructured":"Widmer, C., Kloft, M., Lou, X., R\u00e4tsch, G.: Regularization-based multitask learning with applications to genome biology and biological imaging. KI-K\u00fcnstliche Intelligenz 28(1), 29\u201333 (2014)","journal-title":"KI-K\u00fcnstliche Intelligenz"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Deep Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30484-3_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T23:05:52Z","timestamp":1567983952000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30484-3_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304836","9783030304843"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30484-3_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}